From the 1 of 7 linked papers with an AI index.
7 papers
Extending LLM Context via Associative Recurrent Memory
Gleb Kuzmin, Ivan Rodkin, Aydar Bulatov +8
The paper introduces the Associative Recurrent Memory Transformer (ARMT) to enable large language models to handle much longer contexts with constant memory usage and reduced compu…
Efficient Hallucination Detection for LLMs Using Uncertainty-Aware Attention Heads
Artem Vazhentsev, Lyudmila Rvanova, Gleb Kuzmin +8
While large language models (LLMs) have become highly capable, they remain prone to factual inaccuracies, commonly referred to as "hallucinations." Uncertainty quantification (UQ)…
Exploring Large Language Models for Detecting Mental Disorders
Gleb Kuzmin, Petr Strepetov, Maksim Stankevich +3
This paper compares the effectiveness of traditional machine learning methods, encoder-based models, and large language models (LLMs) on the task of detecting depression and anxiet…
Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models
Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing +7
Uncertainty quantification (UQ) has emerged as a promising approach for detecting hallucinations and low-quality output of Large Language Models (LLMs). However, obtaining proper u…
Diagonal Batching Unlocks Parallelism in Recurrent Memory Transformers for Long Contexts
Danil Sivtsov, Ivan Rodkin, Gleb Kuzmin +2
Transformer models struggle with long-context inference due to their quadratic time and linear memory complexity. Recurrent Memory Transformers (RMTs) offer a solution by reducing…
Inference-Time Selective Debiasing to Enhance Fairness in Text Classification Models
Gleb Kuzmin, Neemesh Yadav, Ivan Smirnov +2
We propose selective debiasing -- an inference-time safety mechanism designed to enhance the overall model quality in terms of prediction performance and fairness, especially in sc…